{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "training_neural_xc_functional.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "JndnmDMp66FL",
        "29bi9k9toyk3"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/google-research/google-research/blob/master/jax_dft/examples/training_neural_xc_functional.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JndnmDMp66FL"
      },
      "source": [
        "##### Copyright 2020 Google LLC.\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hMqWDc_m6rUC",
        "cellView": "both"
      },
      "source": [
        "#@title Default title text\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "29bi9k9toyk3"
      },
      "source": [
        "# Setup\n",
        "\n",
        "Change to GPU runtime: Runtime -> Change runtime type -> Hardware accelerator -> GPU"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tjm2IxJaFTnr",
        "outputId": "954dc1d0-e7e3-44e4-862c-d79f70c54148",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Check cuda version\n",
        "!nvcc --version"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2019 NVIDIA Corporation\n",
            "Built on Sun_Jul_28_19:07:16_PDT_2019\n",
            "Cuda compilation tools, release 10.1, V10.1.243\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nMwgMy0XFgOy"
      },
      "source": [
        "The jaxlib version must correspond to the version of the existing CUDA installation you want to use, with `cuda110` for CUDA 11.0, `cuda102` for CUDA 10.2, `cuda101` for CUDA 10.1, and `cuda100` for CUDA 10.0."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sKBGRy5foj-u",
        "outputId": "7394f2c0-1a37-4d4b-c884-0d38a0d2164a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# For GPU runtime\n",
        "!pip install --upgrade jax jaxlib==0.1.62+cuda110 -f https://storage.googleapis.com/jax-releases/jax_releases.html"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S4pHvFYbo-Nn",
        "outputId": "a3135d1c-1732-4aa9-862b-daa05d14813f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Install jax-dft\n",
        "!git clone https://github.com/google-research/google-research.git\n",
        "!pip install google-research/jax_dft"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8LJQB7-tqHxm",
        "outputId": "87d72ee3-f63d-452d-a4a9-2d2e3b274824",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!rm -rf h2\n",
        "!wget https://github.com/google-research/google-research/raw/master/jax_dft/data/h2.zip\n",
        "!unzip h2.zip"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-10-28 05:59:55--  https://github.com/google-research/google-research/raw/master/jax_dft/data/h2.zip\n",
            "Resolving github.com (github.com)... 140.82.118.4\n",
            "Connecting to github.com (github.com)|140.82.118.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/google-research/google-research/master/jax_dft/data/h2.zip [following]\n",
            "--2020-10-28 05:59:56--  https://raw.githubusercontent.com/google-research/google-research/master/jax_dft/data/h2.zip\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 420194 (410K) [application/zip]\n",
            "Saving to: ‘h2.zip’\n",
            "\n",
            "h2.zip              100%[===================>] 410.35K  --.-KB/s    in 0.02s   \n",
            "\n",
            "2020-10-28 05:59:56 (16.7 MB/s) - ‘h2.zip’ saved [420194/420194]\n",
            "\n",
            "Archive:  h2.zip\n",
            "   creating: h2/\n",
            "  inflating: h2/total_energies.npy   \n",
            "  inflating: h2/grids.npy            \n",
            "  inflating: h2/locations.npy        \n",
            "  inflating: h2/nuclear_charges.npy  \n",
            "  inflating: h2/external_potentials.npy  \n",
            "  inflating: h2/exact_v_s.npy        \n",
            "  inflating: h2/num_electrons.npy    \n",
            "  inflating: h2/distances_x100.npy   \n",
            "  inflating: h2/densities.npy        \n",
            "  inflating: h2/distances.npy        \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XcsyETqVV80s",
        "outputId": "4ee26d16-3338-40a5-d1af-9a6410fc42d2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!wget https://github.com/google-research/google-research/raw/master/jax_dft/data/h2_optimal.pkl"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-10-28 05:59:56--  https://github.com/google-research/google-research/raw/master/jax_dft/data/h2_optimal.pkl\n",
            "Resolving github.com (github.com)... 140.82.121.4\n",
            "Connecting to github.com (github.com)|140.82.121.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/google-research/google-research/master/jax_dft/data/h2_optimal.pkl [following]\n",
            "--2020-10-28 05:59:56--  https://raw.githubusercontent.com/google-research/google-research/master/jax_dft/data/h2_optimal.pkl\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 12905 (13K) [application/octet-stream]\n",
            "Saving to: ‘h2_optimal.pkl’\n",
            "\n",
            "h2_optimal.pkl      100%[===================>]  12.60K  --.-KB/s    in 0s      \n",
            "\n",
            "2020-10-28 05:59:56 (97.7 MB/s) - ‘h2_optimal.pkl’ saved [12905/12905]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XKioKmifrJb4"
      },
      "source": [
        "import os\n",
        "os.environ['XLA_FLAGS'] = '--xla_gpu_deterministic_reductions'\n",
        "\n",
        "import glob\n",
        "import pickle\n",
        "import time\n",
        "import jax\n",
        "from jax import random\n",
        "from jax import tree_util\n",
        "from jax import config\n",
        "import jax.numpy as jnp\n",
        "from jax_dft import datasets\n",
        "from jax_dft import jit_scf\n",
        "from jax_dft import losses\n",
        "from jax_dft import neural_xc\n",
        "from jax_dft import np_utils\n",
        "from jax_dft import scf\n",
        "from jax_dft import utils\n",
        "from jax_dft import xc\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import scipy\n",
        "\n",
        "# Set the default dtype as float64\n",
        "config.update('jax_enable_x64', True)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "omW3f63atHYk",
        "outputId": "190d612c-b176-47af-e07f-f3d4589fc2f8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(f'JAX devices: {jax.devices()}')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "JAX devices: [GpuDevice(id=0)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uyyYbc9RmD5A"
      },
      "source": [
        "# Load data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ySNEoTIvQR8T"
      },
      "source": [
        "train_distances = [128, 384]  #@param\n",
        "\n",
        "dataset = datasets.Dataset(path='h2/', num_grids=513)\n",
        "grids = dataset.grids\n",
        "train_set = dataset.get_molecules(train_distances)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UrKCcSollHvM"
      },
      "source": [
        "#@title Check distances are symmetric\n",
        "if not np.all(utils.location_center_at_grids_center_point(\n",
        "    train_set.locations, grids)):\n",
        "  raise ValueError(\n",
        "      'Training set contains examples '\n",
        "      'not centered at the center of the grids.')"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fw8l4za7p8XD"
      },
      "source": [
        "#@title Initial density\n",
        "initial_density = scf.get_initial_density(train_set, method='noninteracting')"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "35ewa1TXHRIZ"
      },
      "source": [
        "# Train neural XC functional with KS regularizer\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JtYLxZpd-ML3"
      },
      "source": [
        "![neural_xc_architecture.png]()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "id6VniEHr7vn",
        "outputId": "63d05e38-118f-40df-87cd-73524dfd6bb0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#@title Initialize network\n",
        "network = neural_xc.build_global_local_conv_net(\n",
        "    num_global_filters=16,\n",
        "    num_local_filters=16,\n",
        "    num_local_conv_layers=2,\n",
        "    activation='swish',\n",
        "    grids=grids,\n",
        "    minval=0.1,\n",
        "    maxval=2.385345,\n",
        "    downsample_factor=0)\n",
        "network = neural_xc.wrap_network_with_self_interaction_layer(\n",
        "    network, grids=grids, interaction_fn=utils.exponential_coulomb)\n",
        "init_fn, neural_xc_energy_density_fn = neural_xc.global_functional(\n",
        "    network, grids=grids)\n",
        "init_params = init_fn(random.PRNGKey(0))\n",
        "initial_checkpoint_index = 0\n",
        "spec, flatten_init_params = np_utils.flatten(init_params)\n",
        "print(f'number of parameters: {len(flatten_init_params)}')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "number of parameters: 1568\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UWVGAf7P-wYZ"
      },
      "source": [
        "![ks_flow.png]()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E6HZc3EVxtEz",
        "cellView": "form"
      },
      "source": [
        "#@markdown The number of Kohn-Sham iterations in training.\n",
        "num_iterations = 15 #@param{'type': 'integer'}\n",
        "#@markdown The density linear mixing factor.\n",
        "alpha = 0.5 #@param{'type': 'number'}\n",
        "#@markdown Decay factor of density linear mixing factor.\n",
        "alpha_decay = 0.9 #@param{'type': 'number'}\n",
        "#@markdown The number of density differences in the previous iterations to mix the\n",
        "#@markdown density. Linear mixing is num_mixing_iterations = 1.\n",
        "num_mixing_iterations = 1 #@param{'type': 'integer'}\n",
        "#@markdown The stopping criteria of Kohn-Sham iteration on density.\n",
        "density_mse_converge_tolerance = -1. #@param{'type': 'number'}\n",
        "#@markdown Apply stop gradient on the output state of this step and all steps\n",
        "#@markdown before. The first KS step is indexed as 0. Default -1, no stop gradient\n",
        "#@markdown is applied.\n",
        "stop_gradient_step=-1 #@param{'type': 'integer'}\n",
        "\n",
        "def _kohn_sham(flatten_params, locations, nuclear_charges, initial_density):\n",
        "  return jit_scf.kohn_sham(\n",
        "      locations=locations,\n",
        "      nuclear_charges=nuclear_charges,\n",
        "      num_electrons=dataset.num_electrons,\n",
        "      num_iterations=num_iterations,\n",
        "      grids=grids,\n",
        "      xc_energy_density_fn=tree_util.Partial(\n",
        "          neural_xc_energy_density_fn,\n",
        "          params=np_utils.unflatten(spec, flatten_params)),\n",
        "      interaction_fn=utils.exponential_coulomb,\n",
        "      # The initial density of KS self-consistent calculations.\n",
        "      initial_density=initial_density,\n",
        "      alpha=alpha,\n",
        "      alpha_decay=alpha_decay,\n",
        "      enforce_reflection_symmetry=True,\n",
        "      num_mixing_iterations=num_mixing_iterations,\n",
        "      density_mse_converge_tolerance=density_mse_converge_tolerance,\n",
        "      stop_gradient_step=stop_gradient_step)\n",
        "_batch_jit_kohn_sham = jax.vmap(_kohn_sham, in_axes=(None, 0, 0, 0))\n",
        "\n",
        "grids_integration_factor = utils.get_dx(grids) * len(grids)\n",
        "\n",
        "def loss_fn(\n",
        "    flatten_params, locations, nuclear_charges,\n",
        "    initial_density, target_energy, target_density):\n",
        "  \"\"\"Get losses.\"\"\"\n",
        "  states = _batch_jit_kohn_sham(\n",
        "      flatten_params, locations, nuclear_charges, initial_density)\n",
        "  # Energy loss\n",
        "  loss_value = losses.trajectory_mse(\n",
        "      target=target_energy,\n",
        "      predict=states.total_energy[\n",
        "          # The starting states have larger errors. Ignore the number of \n",
        "          # starting states (here 10) in loss.\n",
        "          :, 10:],\n",
        "      # The discount factor in the trajectory loss.\n",
        "      discount=0.9) / dataset.num_electrons\n",
        "  # Density loss\n",
        "  loss_value += losses.mean_square_error(\n",
        "      target=target_density, predict=states.density[:, -1, :]\n",
        "      ) * grids_integration_factor / dataset.num_electrons\n",
        "  return loss_value\n",
        "\n",
        "value_and_grad_fn = jax.jit(jax.value_and_grad(loss_fn))\n",
        "\n",
        "#@markdown The frequency of saving checkpoints.\n",
        "save_every_n = 20 #@param{'type': 'integer'}\n",
        "\n",
        "loss_record = []\n",
        "\n",
        "def np_value_and_grad_fn(flatten_params):\n",
        "  \"\"\"Gets loss value and gradient of parameters as float and numpy array.\"\"\"\n",
        "  start_time = time.time()\n",
        "  # Automatic differentiation.\n",
        "  train_set_loss, train_set_gradient = value_and_grad_fn(\n",
        "      flatten_params,\n",
        "      locations=train_set.locations,\n",
        "      nuclear_charges=train_set.nuclear_charges,\n",
        "      initial_density=initial_density,\n",
        "      target_energy=train_set.total_energy,\n",
        "      target_density=train_set.density)\n",
        "  step_time = time.time() - start_time\n",
        "  step = initial_checkpoint_index + len(loss_record)\n",
        "  print(f'step {step}, loss {train_set_loss} in {step_time} sec')\n",
        "\n",
        "  # Save checkpoints.\n",
        "  if len(loss_record) % save_every_n == 0:\n",
        "    checkpoint_path = f'ckpt-{step:05d}'\n",
        "    print(f'Save checkpoint {checkpoint_path}')\n",
        "    with open(checkpoint_path, 'wb') as handle:\n",
        "      pickle.dump(np_utils.unflatten(spec, flatten_params), handle)\n",
        "\n",
        "  loss_record.append(train_set_loss)\n",
        "  return train_set_loss, np.array(train_set_gradient)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KZO4Rrzotax4",
        "cellView": "form",
        "outputId": "216a05ac-cca1-4259-8b79-faca10c6d07e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#@title Use L-BFGS optimizer to update neural network functional\n",
        "#@markdown This cell trains the model. Each step takes about 1.6s.\n",
        "max_train_steps = 200 #@param{'type': 'integer'}\n",
        "\n",
        "_, _, info = scipy.optimize.fmin_l_bfgs_b(\n",
        "    np_value_and_grad_fn,\n",
        "    x0=np.array(flatten_init_params),\n",
        "    # Maximum number of function evaluations.\n",
        "    maxfun=max_train_steps,\n",
        "    factr=1,\n",
        "    m=20,\n",
        "    pgtol=1e-14)\n",
        "print(info)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "step 0, loss 0.18682592209915364 in 18.91426110267639 sec\n",
            "Save checkpoint ckpt-00000\n",
            "step 1, loss 2.206830723734567 in 1.6032278537750244 sec\n",
            "step 2, loss 0.03004158671185065 in 1.599245309829712 sec\n",
            "step 3, loss 0.02553093700506681 in 1.601923942565918 sec\n",
            "step 4, loss 0.02459630093506849 in 1.6012306213378906 sec\n",
            "step 5, loss 0.017675425897103497 in 1.601691722869873 sec\n",
            "step 6, loss 0.013219944665877998 in 1.6036527156829834 sec\n",
            "step 7, loss 0.011388247872334965 in 1.5963976383209229 sec\n",
            "step 8, loss 0.009059642195789086 in 1.595712661743164 sec\n",
            "step 9, loss 0.008083928628033784 in 1.5929219722747803 sec\n",
            "step 10, loss 0.007451996138282887 in 1.592787742614746 sec\n",
            "step 11, loss 0.00563641708716168 in 1.5941333770751953 sec\n",
            "step 12, loss 0.004382577906390241 in 1.5926611423492432 sec\n",
            "step 13, loss 0.00670619981053802 in 1.5969011783599854 sec\n",
            "step 14, loss 0.004218570636327058 in 1.5960896015167236 sec\n",
            "step 15, loss 0.0041349648394521455 in 1.5986874103546143 sec\n",
            "step 16, loss 0.004095235694882229 in 1.5935182571411133 sec\n",
            "step 17, loss 0.004091652704184595 in 1.5969128608703613 sec\n",
            "step 18, loss 0.0040823469556244656 in 1.6017084121704102 sec\n",
            "step 19, loss 0.004059217215577169 in 1.6001510620117188 sec\n",
            "step 20, loss 0.003994579137341326 in 1.6013822555541992 sec\n",
            "Save checkpoint ckpt-00020\n",
            "step 21, loss 0.003596117803138924 in 1.6018409729003906 sec\n",
            "step 22, loss 0.007852783587967965 in 1.608626365661621 sec\n",
            "step 23, loss 0.0033755205742547686 in 1.6038925647735596 sec\n",
            "step 24, loss 0.03191464856865315 in 1.6019008159637451 sec\n",
            "step 25, loss 0.0032971746524187357 in 1.600766897201538 sec\n",
            "step 26, loss 0.0030098765316007247 in 1.612178087234497 sec\n",
            "step 27, loss 0.003285741751264644 in 1.6082797050476074 sec\n",
            "step 28, loss 0.00249939591665361 in 1.6088435649871826 sec\n",
            "step 29, loss 0.0026789289298903753 in 1.6037750244140625 sec\n",
            "step 30, loss 0.00243366489564952 in 1.5999197959899902 sec\n",
            "step 31, loss 0.002313862191121909 in 1.5972154140472412 sec\n",
            "step 32, loss 0.0019374303238232106 in 1.599487066268921 sec\n",
            "step 33, loss 0.0015588037679206204 in 1.5944736003875732 sec\n",
            "step 34, loss 0.0015136270375735539 in 1.5953986644744873 sec\n",
            "step 35, loss 0.019939355006531392 in 1.598560094833374 sec\n",
            "step 36, loss 0.0010343375078548105 in 1.5967719554901123 sec\n",
            "step 37, loss 0.001513105086707659 in 1.601255178451538 sec\n",
            "step 38, loss 0.0009849859485439303 in 1.601139783859253 sec\n",
            "step 39, loss 0.0010001712814671636 in 1.602048635482788 sec\n",
            "step 40, loss 0.000916131520373706 in 1.595008373260498 sec\n",
            "Save checkpoint ckpt-00040\n",
            "step 41, loss 0.0008721082284643743 in 1.5969867706298828 sec\n",
            "step 42, loss 0.0008283509900547969 in 1.599623203277588 sec\n",
            "step 43, loss 0.0007648171529297379 in 1.5999982357025146 sec\n",
            "step 44, loss 0.000733857601175496 in 1.5995550155639648 sec\n",
            "step 45, loss 0.0007129601383377773 in 1.5958302021026611 sec\n",
            "step 46, loss 0.000669976311236243 in 1.6097354888916016 sec\n",
            "step 47, loss 0.00063276476618544 in 1.6052360534667969 sec\n",
            "step 48, loss 0.0005907613063642969 in 1.6079919338226318 sec\n",
            "step 49, loss 0.0005273476529145232 in 1.6090891361236572 sec\n",
            "step 50, loss 0.0004152102013867578 in 1.6066012382507324 sec\n",
            "step 51, loss 0.00901970299605296 in 1.606797695159912 sec\n",
            "step 52, loss 0.0003704741603659335 in 1.6034460067749023 sec\n",
            "step 53, loss 0.0006018047167388527 in 1.603362798690796 sec\n",
            "step 54, loss 0.00033361066073013133 in 1.6058545112609863 sec\n",
            "step 55, loss 0.0003293665984319947 in 1.602440357208252 sec\n",
            "step 56, loss 0.00032597081228207306 in 1.6051044464111328 sec\n",
            "step 57, loss 0.0003184983680543874 in 1.6038920879364014 sec\n",
            "step 58, loss 0.0003140053830567265 in 1.6053779125213623 sec\n",
            "step 59, loss 0.0003129644617494567 in 1.6077580451965332 sec\n",
            "step 60, loss 0.0003116439330152512 in 1.6060059070587158 sec\n",
            "Save checkpoint ckpt-00060\n",
            "step 61, loss 0.000310264399222947 in 1.606128454208374 sec\n",
            "step 62, loss 0.0003087774510021864 in 1.6054151058197021 sec\n",
            "step 63, loss 0.00030704415112293396 in 1.6115269660949707 sec\n",
            "step 64, loss 0.0003055220161828725 in 1.6068272590637207 sec\n",
            "step 65, loss 0.0003031727933027568 in 1.6078598499298096 sec\n",
            "step 66, loss 0.00029730483595830515 in 1.608546257019043 sec\n",
            "step 67, loss 0.002334480921365621 in 1.6057684421539307 sec\n",
            "step 68, loss 0.00029016680283818467 in 1.6083221435546875 sec\n",
            "step 69, loss 0.0023562776721753917 in 1.6128191947937012 sec\n",
            "step 70, loss 0.000284411644742761 in 1.6121325492858887 sec\n",
            "step 71, loss 0.00034927295036073035 in 1.609246015548706 sec\n",
            "step 72, loss 0.00027719621585470906 in 1.603830099105835 sec\n",
            "step 73, loss 0.00028268039012792405 in 1.6077516078948975 sec\n",
            "step 74, loss 0.00027252070087934734 in 1.6105732917785645 sec\n",
            "step 75, loss 0.0002635829371859435 in 1.6085350513458252 sec\n",
            "step 76, loss 0.0002459710013312932 in 1.6082797050476074 sec\n",
            "step 77, loss 0.0002144354699632871 in 1.6095991134643555 sec\n",
            "step 78, loss 0.00036988037301344116 in 1.6105914115905762 sec\n",
            "step 79, loss 0.00017953833215357445 in 1.606307029724121 sec\n",
            "step 80, loss 0.00022340545844951235 in 1.6059520244598389 sec\n",
            "Save checkpoint ckpt-00080\n",
            "step 81, loss 0.00017654105669896128 in 1.6036629676818848 sec\n",
            "step 82, loss 0.000183761845848182 in 1.6068432331085205 sec\n",
            "step 83, loss 0.00017307111181272432 in 1.605792760848999 sec\n",
            "step 84, loss 0.0001690355720019124 in 1.606916904449463 sec\n",
            "step 85, loss 0.0001642570327008637 in 1.6058919429779053 sec\n",
            "step 86, loss 0.0001617833537905984 in 1.6061391830444336 sec\n",
            "step 87, loss 0.00015999956958377516 in 1.6053764820098877 sec\n",
            "step 88, loss 0.0001585240360213794 in 1.6124796867370605 sec\n",
            "step 89, loss 0.00015831082231015497 in 1.6075115203857422 sec\n",
            "step 90, loss 0.00015747306226414817 in 1.610612392425537 sec\n",
            "step 91, loss 0.00015729025961443152 in 1.6062474250793457 sec\n",
            "step 92, loss 0.00015704139218248217 in 1.604130744934082 sec\n",
            "step 93, loss 0.0001569313575996839 in 1.6065449714660645 sec\n",
            "step 94, loss 0.00015648697775945658 in 1.609889030456543 sec\n",
            "step 95, loss 0.0001549489765184351 in 1.607015609741211 sec\n",
            "step 96, loss 0.00023778624793207394 in 1.6078474521636963 sec\n",
            "step 97, loss 0.00015409129507762238 in 1.6110072135925293 sec\n",
            "step 98, loss 0.000151448552069971 in 1.6068580150604248 sec\n",
            "step 99, loss 0.00014569561221334715 in 1.6070318222045898 sec\n",
            "step 100, loss 0.00013885471293794568 in 1.6080632209777832 sec\n",
            "Save checkpoint ckpt-00100\n",
            "step 101, loss 0.00013433798008588707 in 1.6109421253204346 sec\n",
            "step 102, loss 0.00013707429181875332 in 1.6084697246551514 sec\n",
            "step 103, loss 0.0001321799071969696 in 1.603865385055542 sec\n",
            "step 104, loss 0.000127773158625459 in 1.6082088947296143 sec\n",
            "step 105, loss 0.005187804146663048 in 1.6093268394470215 sec\n",
            "step 106, loss 0.00012586435641870103 in 1.6056132316589355 sec\n",
            "step 107, loss 0.00012498303175879317 in 1.6052687168121338 sec\n",
            "step 108, loss 0.00031332489368956996 in 1.607393503189087 sec\n",
            "step 109, loss 0.00012279641871292736 in 1.6062672138214111 sec\n",
            "step 110, loss 0.00017200083983599324 in 1.610658884048462 sec\n",
            "step 111, loss 0.0001195500523569489 in 1.6051476001739502 sec\n",
            "step 112, loss 0.00011288037717334405 in 1.6068158149719238 sec\n",
            "step 113, loss 0.0005156655450802882 in 1.610935926437378 sec\n",
            "step 114, loss 0.00011029824759251398 in 1.6137988567352295 sec\n",
            "step 115, loss 0.00010734058591999949 in 1.6098239421844482 sec\n",
            "step 116, loss 0.00010436311988800968 in 1.6065537929534912 sec\n",
            "step 117, loss 0.00010169348862232266 in 1.605855941772461 sec\n",
            "step 118, loss 0.00010118419143046733 in 1.610891580581665 sec\n",
            "step 119, loss 0.00010037091183779778 in 1.605926513671875 sec\n",
            "step 120, loss 9.989157958705135e-05 in 1.608501672744751 sec\n",
            "Save checkpoint ckpt-00120\n",
            "step 121, loss 9.85588098041587e-05 in 1.609349250793457 sec\n",
            "step 122, loss 9.770367451757206e-05 in 1.6125268936157227 sec\n",
            "step 123, loss 9.59246395097861e-05 in 1.6109263896942139 sec\n",
            "step 124, loss 9.397077630634391e-05 in 1.6068193912506104 sec\n",
            "step 125, loss 0.00012600154804407867 in 1.6077601909637451 sec\n",
            "step 126, loss 9.261573107589782e-05 in 1.608579397201538 sec\n",
            "step 127, loss 9.858496174307825e-05 in 1.613098382949829 sec\n",
            "step 128, loss 9.151654876866553e-05 in 1.6145737171173096 sec\n",
            "step 129, loss 9.043230682951342e-05 in 1.6110022068023682 sec\n",
            "step 130, loss 8.985160109067744e-05 in 1.6137068271636963 sec\n",
            "step 131, loss 8.950616408103753e-05 in 1.6097049713134766 sec\n",
            "step 132, loss 8.822417034514313e-05 in 1.6094238758087158 sec\n",
            "step 133, loss 8.73207088951682e-05 in 1.6114957332611084 sec\n",
            "step 134, loss 8.950739862901297e-05 in 1.6152324676513672 sec\n",
            "step 135, loss 8.672850062401176e-05 in 1.6171932220458984 sec\n",
            "step 136, loss 8.508722682395833e-05 in 1.6097314357757568 sec\n",
            "step 137, loss 8.277348413365366e-05 in 1.6105339527130127 sec\n",
            "step 138, loss 8.093295774971481e-05 in 1.6085638999938965 sec\n",
            "step 139, loss 0.005457445822021816 in 1.6075077056884766 sec\n",
            "step 140, loss 8.389978860487413e-05 in 1.6077184677124023 sec\n",
            "Save checkpoint ckpt-00140\n",
            "step 141, loss 7.98013293360733e-05 in 1.6078672409057617 sec\n",
            "step 142, loss 8.460895195954944e-05 in 1.6109955310821533 sec\n",
            "step 143, loss 7.940770453805717e-05 in 1.6157910823822021 sec\n",
            "step 144, loss 7.876763030136511e-05 in 1.6115317344665527 sec\n",
            "step 145, loss 7.751490473727539e-05 in 1.6104016304016113 sec\n",
            "step 146, loss 7.86474660411895e-05 in 1.6085810661315918 sec\n",
            "step 147, loss 7.705525751587382e-05 in 1.612553596496582 sec\n",
            "step 148, loss 7.719023392109552e-05 in 1.6112544536590576 sec\n",
            "step 149, loss 7.61307491553337e-05 in 1.6112279891967773 sec\n",
            "step 150, loss 7.497503447074029e-05 in 1.6119825839996338 sec\n",
            "step 151, loss 7.458098268206766e-05 in 1.6092164516448975 sec\n",
            "step 152, loss 7.308969040707132e-05 in 1.6097826957702637 sec\n",
            "step 153, loss 7.815157348278869e-05 in 1.6133177280426025 sec\n",
            "step 154, loss 7.261559473020562e-05 in 1.6077144145965576 sec\n",
            "step 155, loss 7.177750358808986e-05 in 1.6132373809814453 sec\n",
            "step 156, loss 7.048406443794219e-05 in 1.6110889911651611 sec\n",
            "step 157, loss 7.037235747624733e-05 in 1.607356071472168 sec\n",
            "step 158, loss 6.983661688572147e-05 in 1.6109673976898193 sec\n",
            "step 159, loss 6.839132690029285e-05 in 1.6080315113067627 sec\n",
            "step 160, loss 6.730003086191184e-05 in 1.6084110736846924 sec\n",
            "Save checkpoint ckpt-00160\n",
            "step 161, loss 6.521983989002649e-05 in 1.607130765914917 sec\n",
            "step 162, loss 0.00033546733553573515 in 1.6116273403167725 sec\n",
            "step 163, loss 6.430308151180325e-05 in 1.6095757484436035 sec\n",
            "step 164, loss 6.32350273358548e-05 in 1.6106703281402588 sec\n",
            "step 165, loss 0.0001043334893288657 in 1.6108009815216064 sec\n",
            "step 166, loss 6.254123751937172e-05 in 1.605820894241333 sec\n",
            "step 167, loss 6.204443608543746e-05 in 1.606334924697876 sec\n",
            "step 168, loss 6.163790469825103e-05 in 1.6100873947143555 sec\n",
            "step 169, loss 6.0935987953765175e-05 in 1.6075129508972168 sec\n",
            "step 170, loss 6.038321408296158e-05 in 1.605966567993164 sec\n",
            "step 171, loss 6.040728925433862e-05 in 1.6093480587005615 sec\n",
            "step 172, loss 5.97791296098419e-05 in 1.6051666736602783 sec\n",
            "step 173, loss 5.9216389336317455e-05 in 1.6095218658447266 sec\n",
            "step 174, loss 5.9258326784661926e-05 in 1.6096611022949219 sec\n",
            "step 175, loss 5.908367366994026e-05 in 1.6109249591827393 sec\n",
            "step 176, loss 5.8923062203180146e-05 in 1.606597900390625 sec\n",
            "step 177, loss 5.8859640429726666e-05 in 1.61185884475708 sec\n",
            "step 178, loss 5.8744263970002465e-05 in 1.613433837890625 sec\n",
            "step 179, loss 5.8599075785640756e-05 in 1.6068615913391113 sec\n",
            "step 180, loss 5.8447386298126406e-05 in 1.6085503101348877 sec\n",
            "Save checkpoint ckpt-00180\n",
            "step 181, loss 5.848243909213671e-05 in 1.6074628829956055 sec\n",
            "step 182, loss 5.832612601218755e-05 in 1.6089730262756348 sec\n",
            "step 183, loss 5.822386800872816e-05 in 1.6122815608978271 sec\n",
            "step 184, loss 5.8177976974426674e-05 in 1.6099333763122559 sec\n",
            "step 185, loss 5.8103635836626124e-05 in 1.610410213470459 sec\n",
            "step 186, loss 5.783794433985727e-05 in 1.611147165298462 sec\n",
            "step 187, loss 5.9402354570047874e-05 in 1.6079485416412354 sec\n",
            "step 188, loss 5.751105349415675e-05 in 1.6082987785339355 sec\n",
            "step 189, loss 5.79832549701516e-05 in 1.607496738433838 sec\n",
            "step 190, loss 5.7317607468747214e-05 in 1.6115138530731201 sec\n",
            "step 191, loss 5.76943639874525e-05 in 1.6075506210327148 sec\n",
            "step 192, loss 5.703467899898107e-05 in 1.6180651187896729 sec\n",
            "step 193, loss 5.6182511667152724e-05 in 1.6152715682983398 sec\n",
            "step 194, loss 5.555000593957358e-05 in 1.6100258827209473 sec\n",
            "step 195, loss 5.5683175361041737e-05 in 1.612339735031128 sec\n",
            "step 196, loss 5.500556855817121e-05 in 1.6185057163238525 sec\n",
            "step 197, loss 5.488355084067149e-05 in 1.611764907836914 sec\n",
            "step 198, loss 5.47046868561731e-05 in 1.6109700202941895 sec\n",
            "step 199, loss 5.732802096560722e-05 in 1.6170639991760254 sec\n",
            "step 200, loss 5.452602803517365e-05 in 1.6102423667907715 sec\n",
            "Save checkpoint ckpt-00200\n",
            "{'grad': array([ 5.10864146e-06,  9.21505100e-07, -1.33855345e-05, ...,\n",
            "       -1.16788428e-04, -1.39431908e-04,  0.00000000e+00]), 'task': b'STOP: TOTAL NO. of f AND g EVALUATIONS EXCEEDS LIMIT', 'funcalls': 201, 'nit': 148, 'warnflag': 1}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GXWzZXfaC98N",
        "outputId": "ac1d6bea-c7e9-426e-b0df-ce99e11cdae4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "#@title loss curve\n",
        "\n",
        "plt.plot(np.minimum.accumulate(loss_record))\n",
        "plt.yscale('log')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('training steps')\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aa-yHJH6L0UE"
      },
      "source": [
        "# Visualize the model prediction on H$_2$ over training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xif2L30qMm74",
        "cellView": "form"
      },
      "source": [
        "#@title Helper functions\n",
        "\n",
        "plot_distances = [40, 56, 72, 88, 104, 120, 136, 152, 184, 200, 216, 232, 248, 264, 280, 312, 328, 344, 360, 376, 392, 408, 424, 456, 472, 488, 504, 520, 536, 568, 584, 600] #@param\n",
        "plot_set = dataset.get_molecules(plot_distances)\n",
        "plot_initial_density = scf.get_initial_density(\n",
        "    plot_set, method='noninteracting')\n",
        "nuclear_energy = utils.get_nuclear_interaction_energy_batch(\n",
        "    plot_set.locations,\n",
        "    plot_set.nuclear_charges,\n",
        "    interaction_fn=utils.exponential_coulomb)\n",
        "\n",
        "def kohn_sham(\n",
        "    params, locations, nuclear_charges, initial_density=None, use_lda=False):\n",
        "  return scf.kohn_sham(\n",
        "    locations=locations,\n",
        "    nuclear_charges=nuclear_charges,\n",
        "    num_electrons=dataset.num_electrons,\n",
        "    num_iterations=num_iterations,\n",
        "    grids=grids,\n",
        "    xc_energy_density_fn=tree_util.Partial(\n",
        "        xc.get_lda_xc_energy_density_fn() if use_lda else neural_xc_energy_density_fn,\n",
        "        params=params),\n",
        "    interaction_fn=utils.exponential_coulomb,\n",
        "    # The initial density of KS self-consistent calculations.\n",
        "    initial_density=initial_density,\n",
        "    alpha=alpha,\n",
        "    alpha_decay=alpha_decay,\n",
        "    enforce_reflection_symmetry=True,\n",
        "    num_mixing_iterations=num_mixing_iterations,\n",
        "    density_mse_converge_tolerance=density_mse_converge_tolerance)\n",
        "\n",
        "def get_states(ckpt_path):\n",
        "  print(f'Load {ckpt_path}')\n",
        "  with open(ckpt_path, 'rb') as handle:\n",
        "    params = pickle.load(handle)\n",
        "  states = []\n",
        "  for i in range(len(plot_distances)):\n",
        "    states.append(kohn_sham(\n",
        "        params,\n",
        "        locations=plot_set.locations[i],\n",
        "        nuclear_charges=plot_set.nuclear_charges[i],\n",
        "        initial_density=plot_initial_density[i]))\n",
        "  return tree_util.tree_multimap(lambda *x: jnp.stack(x), *states)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3BofHDm0qc4e",
        "outputId": "125d2cbb-ab68-4f30-83d9-e35c2392380a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#@title Distribution of the model trained with Kohn-Sham regularizer\n",
        "#@markdown Runtime ~20 minutes for 11 checkpoints.\n",
        "#@markdown To speed up the calculation, you can reduce the number of\n",
        "#@markdown separations to compute in\n",
        "#@markdown `Helper functions -> plot_distances`\n",
        "\n",
        "ckpt_list = sorted(glob.glob('ckpt-?????'))\n",
        "num_ckpts = len(ckpt_list)\n",
        "ckpt_states = []\n",
        "for ckpt_path in ckpt_list:\n",
        "  ckpt_states.append(get_states(ckpt_path))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Load ckpt-00000\n",
            "Load ckpt-00020\n",
            "Load ckpt-00040\n",
            "Load ckpt-00060\n",
            "Load ckpt-00080\n",
            "Load ckpt-00100\n",
            "Load ckpt-00120\n",
            "Load ckpt-00140\n",
            "Load ckpt-00160\n",
            "Load ckpt-00180\n",
            "Load ckpt-00200\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BzYnxr5f3ZBL",
        "outputId": "ea4590f3-bce9-4727-d308-a7c8d59c7823",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "for i, (states, ckpt_path) in enumerate(zip(ckpt_states, ckpt_list)):\n",
        "  plt.plot(\n",
        "      np.array(plot_distances) / 100,\n",
        "      nuclear_energy + states.total_energy[:, -1],\n",
        "      color=str(0.1 + 0.85 * (num_ckpts - i) / num_ckpts),\n",
        "      label=ckpt_path)\n",
        "plt.plot(\n",
        "    np.array(plot_distances) / 100,\n",
        "    nuclear_energy + plot_set.total_energy,\n",
        "    c='r', dashes=(10, 8), label='exact')\n",
        "plt.xlabel(r'$R\\,\\,\\mathrm{(Bohr)}$')\n",
        "plt.ylabel(r'$E+E_\\mathrm{nn}\\,\\,\\mathsf{(Hartree)}$')\n",
        "plt.legend(bbox_to_anchor=(1.4, 0.8), framealpha=0.5)\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1B0K0ykfYzQ3"
      },
      "source": [
        "# Visualize the optimal checkpoint in paper\n",
        "\n",
        "Here we use the neural XC functional trained with Kohn-Sham regularizer in\n",
        "\n",
        "```\n",
        "Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics\n",
        "Li Li, Stephan Hoyer, Ryan Pederson, Ruoxi Sun, Ekin D. Cubuk, Patrick Riley, and Kieron Burke \n",
        "https://arxiv.org/abs/2009.08551\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hJAq7MCg1cj1"
      },
      "source": [
        "## H2 dissociation curve"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iczZhIAkZLCw",
        "outputId": "a1f3bbfa-9772-49d4-c360-a7776775969d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "states = get_states('h2_optimal.pkl')"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Load h2_optimal.pkl\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GmFoqbUeCalU",
        "outputId": "e4e1fa2e-bfc0-4047-a245-63d27294547b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plt.plot(\n",
        "    np.array(plot_distances) / 100,\n",
        "    nuclear_energy + states.total_energy[:, -1], lw=2.5, label='KSR')\n",
        "plt.plot(\n",
        "    np.array(plot_distances) / 100,\n",
        "    nuclear_energy + plot_set.total_energy,\n",
        "    c='r', dashes=(10, 8), label='exact')\n",
        "plt.xlabel(r'$R\\,\\,\\mathrm{(Bohr)}$')\n",
        "plt.ylabel(r'$E+E_\\mathrm{nn}\\,\\,\\mathsf{(Hartree)}$')\n",
        "plt.legend(loc=0)\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2igUM2Jv1hRK"
      },
      "source": [
        "## Solve one H2 separation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ya3AmmV2aAWd"
      },
      "source": [
        "distance_x100 = 400 #@param{'type': 'integer'}\n",
        "#@markdown Plot range on x axis\n",
        "x_min = -10 #@param{'type': 'number'}\n",
        "x_max = 10 #@param{'type': 'number'}\n",
        "\n",
        "with open('h2_optimal.pkl', 'rb') as handle:\n",
        "  params = pickle.load(handle)\n",
        "\n",
        "test = dataset.get_molecules([distance_x100])"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WMW9O7hA1qq8"
      },
      "source": [
        "### Neural XC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kAosvFm60iiU"
      },
      "source": [
        "solution = kohn_sham(\n",
        "    params,\n",
        "    locations=test.locations[0],\n",
        "    nuclear_charges=test.nuclear_charges[0])"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4BNO5jbGo-YS",
        "outputId": "2c044c67-2ce5-4238-b148-ac139742857e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        }
      },
      "source": [
        "# Density and XC energy density\n",
        "_, axs = plt.subplots(\n",
        "    nrows=3,\n",
        "    ncols=num_iterations // 3,\n",
        "    figsize=(2.5 * (num_iterations // 3), 6), sharex=True, sharey=True)\n",
        "axs[2][2].set_xlabel('x')\n",
        "for i, ax in enumerate(axs.ravel()):\n",
        "  ax.set_title(f'KS iter {i + 1}')\n",
        "  ax.plot(grids, solution.density[i], label=r'$n$')\n",
        "  ax.plot(grids, test.density[0], 'k--', label=r'exact $n$')\n",
        "  ax.plot(grids, solution.xc_energy_density[i], label=r'$\\epsilon_\\mathrm{XC}$')\n",
        "  ax.set_xlim(x_min, x_max)\n",
        "axs[2][-1].legend(bbox_to_anchor=(1.2, 0.8))\n",
        "axs[1][0].set_ylabel('Neural XC')\n",
        "plt.show()\n",
        "\n",
        "plt.plot(\n",
        "    1 + np.arange(num_iterations), solution.total_energy,\n",
        "    label='KS')\n",
        "truth = test.total_energy[0]\n",
        "plt.axhline(y=truth, ls='--', color='k', label='exact')\n",
        "plt.axhspan(\n",
        "    truth - 0.0016, truth + 0.0016, color='0.9', label='chemical accuracy')\n",
        "plt.xlabel('KS iterations')\n",
        "plt.ylabel('Energy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 900x432 with 15 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Si9w-2UC1wjs"
      },
      "source": [
        "### Local density approximation (LDA)\n",
        "\n",
        "As a comparison, we show the solution on the same molecule using LDA functional:\n",
        "\n",
        "```\n",
        "One-dimensional mimicking of electronic structure: The case for exponentials\n",
        "Thomas E. Baker, E. Miles Stoudenmire, Lucas O. Wagner, Kieron Burke, and Steven R. White\n",
        "Physical Review B 91.23 (2015): 235141.\n",
        "```\n",
        "\n",
        "The LDA functional is implemented in the `jax_dft.xc` module."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f2Qy27xqo4jQ"
      },
      "source": [
        "lda = kohn_sham(\n",
        "    None,\n",
        "    locations=test.locations[0],\n",
        "    nuclear_charges=test.nuclear_charges[0],\n",
        "    use_lda=True)"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E0Up0ssj0yyw",
        "outputId": "227451a2-7d17-4183-8984-e8d3d15640ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        }
      },
      "source": [
        "_, axs = plt.subplots(\n",
        "    nrows=3,\n",
        "    ncols=num_iterations // 3,\n",
        "    figsize=(2.5 * (num_iterations // 3), 6), sharex=True, sharey=True)\n",
        "axs[2][2].set_xlabel('x')\n",
        "for i, ax in enumerate(axs.ravel()):\n",
        "  ax.set_title(f'KS iter {i + 1}')\n",
        "  ax.plot(grids, lda.density[i], label=r'$n$')\n",
        "  ax.plot(grids, test.density[0], 'k--', label=r'exact $n$')\n",
        "  ax.plot(grids, lda.xc_energy_density[i], label=r'$\\epsilon_\\mathrm{XC}$')\n",
        "  ax.set_xlim(x_min, x_max)\n",
        "axs[2][-1].legend(bbox_to_anchor=(1.2, 0.8))\n",
        "axs[1][0].set_ylabel('LDA')\n",
        "plt.show()\n",
        "\n",
        "plt.plot(\n",
        "    1 + np.arange(num_iterations), lda.total_energy,\n",
        "    label='KS')\n",
        "truth = test.total_energy[0]\n",
        "plt.axhline(y=truth, ls='--', color='k', label='exact')\n",
        "plt.axhspan(\n",
        "    truth - 0.0016, truth + 0.0016, color='0.9', label='chemical accuracy')\n",
        "plt.xlabel('KS iterations')\n",
        "plt.ylabel('Energy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 900x432 with 15 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ZQm-yrC1Yla"
      },
      "source": [
        ""
      ],
      "execution_count": 24,
      "outputs": []
    }
  ]
}